import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data


INPUT_NODE = 784
OUTPUT_NODE = 10
LAYER1_NODE = 500

BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99

REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 20000
MOVING_AVERAGE_DECAY = 0.99

def inference(input_tensor,avg_class,weights1,biases1,weights2,biases2):
    if avg_class == None:
        layer1 = tf.nn.relu(tf.matmul(input_tensor,weights1)+biases1)
        return tf.matmul(layer1,weights2)+biases2
    else:
        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))
        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)

def train(mnist):
    x  = tf.placeholder(tf.float32,[None,INPUT_NODE],name='x-input')
    y_ = tf.placeholder(tf.float32,[None,OUTPUT_NODE],name='y-input')

    weights1 = tf.Variable(tf.truncated_normal([INPUT_NODE,LAYER1_NODE],stddev=0.1))
    biases1 = tf.Variable(tf.constant(0.1,shape=[LAYER1_NODE]))

    weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE,OUTPUT_NODE],stddev=0.1))
    biases2 = tf.Variable(tf.constant(0.1, shape=[OUTPUT_NODE]))

    y = inference(x,None,weights1,biases1,weights2,biases2)

    global_step = tf.Variable(0,trainable=False)

    variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step)

    variable_averages_op  = variable_averages.apply(tf.trainable_variables())

    average_y = inference(x,variable_averages,weights1,biases1,weights2,biases2)

    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits = y,labels = tf.argmax(y_,1))

    cross_entropy_mean = tf.reduce_mean(cross_entropy)

    regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)

    regularization = regularizer(weights1)+regularizer(weights2)

    loss = cross_entropy_mean+regularization

    learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,global_step,mnist.train.num_examples/BATCH_SIZE,LEARNING_RATE_DECAY)

    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)

    with tf.control_dependencies([train_step,variable_averages_op]):
        train_op = tf.no_op(name='train')
        correct_prediction = tf.equal(tf.argmax(average_y,1),tf.argmax(y_,1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

    with tf.Session() as sess:
        tf.global_variables_initializer().run()
        validate_feed = {x:mnist.validation.images,y_:mnist.validation.labels}
        test_feed = {x:mnist.test.images,y_:mnist.test.labels}
        for i in range(TRAINING_STEPS):
            if i%1000 == 0:
                validate_acc = sess.run(accuracy,feed_dict=validate_feed)
                print("%d %.3f"%(i,validate_acc))
            xs,ys = mnist.train.next_batch(BATCH_SIZE)
            sess.run(train_op,feed_dict={x:xs,y_:ys})

        test_acc = sess.run(accuracy,feed_dict=test_feed)
        print("stop",TRAINING_STEPS,test_acc)
def main(argv = None):
    mnist = input_data.read_data_sets("/home/liuchang505/PycharmProjects/Learning/mnist_data/",one_hot = True)
    train(mnist)

if __name__ == '__main__':
    tf.app.run()
